Data pipelines are crucial for processing and transforming data in various domains, including finance, healthcare, and e-commerce. Ensuring the reliability and accuracy of data pipelines is of utmost importance to maintain data integrity and make informed business decisions. In this paper, we explore the significance of continuous monitoring in data pipelines and its contribution to data observability. This work discusses the challenges associated with monitoring data pipelines in real-time, propose a framework for real-time monitoring, and highlight its benefits in enhancing data observability. The findings of this work emphasize the need for organizations to adopt continuous monitoring practices to ensure data quality, detect anomalies, and improve overall system performance.
{"title":"Real-Time Monitoring of Data Pipelines: Exploring and Experimentally Proving that the Continuous Monitoring in Data Pipelines Reduces Cost and Elevates Quality","authors":"Shammy Narayanan, Maheswari S, Prisha Zephan","doi":"10.4108/eetsis.5065","DOIUrl":"https://doi.org/10.4108/eetsis.5065","url":null,"abstract":"Data pipelines are crucial for processing and transforming data in various domains, including finance, healthcare, and e-commerce. Ensuring the reliability and accuracy of data pipelines is of utmost importance to maintain data integrity and make informed business decisions. In this paper, we explore the significance of continuous monitoring in data pipelines and its contribution to data observability. This work discusses the challenges associated with monitoring data pipelines in real-time, propose a framework for real-time monitoring, and highlight its benefits in enhancing data observability. The findings of this work emphasize the need for organizations to adopt continuous monitoring practices to ensure data quality, detect anomalies, and improve overall system performance.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"14 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139797701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Emotion recognition is an immense challenge for immersive technology. In order to detect the emotions of the user, we use machine learning methods and techniques to use the potential of the Virtual Environment and to improve the user Experience. Emotion recognition plays an important role in developing realistic and emotionally immersive experiences in augmented reality (AR) and virtual reality (VR) settings by instantly adjusting interactions, content, and visuals based on the accurate detection and interpretation of users’ emotions. Immersive systems can enhance user experience through various machine learning algorithms and methods used for emotion recognition, which are examined in this article. Upon novel idea, challenges and potential applications of incorporating emotion recognition in immersive virtual environments with Machine Learning (ML) Techniques and the benefits of tailoring powerful immersive experiences with ML methods were highlighted, and also the study discusses potential advancements in identifying the user’s emotion recognition in the future by modeling an Architecture, as well as how the ML techniques were enhanced for virtual environment is discussed.
情绪识别是沉浸式技术面临的巨大挑战。为了检测用户的情绪，我们使用机器学习方法和技术来发挥虚拟环境的潜力，改善用户体验。情绪识别在增强现实（AR）和虚拟现实（VR）环境中开发逼真和情感沉浸式体验方面发挥着重要作用，它可以根据对用户情绪的准确检测和解读，即时调整交互、内容和视觉效果。沉浸式系统可以通过用于情感识别的各种机器学习算法和方法来增强用户体验，本文将对这些算法和方法进行研究。研究强调了利用机器学习（ML）技术将情感识别纳入沉浸式虚拟环境的新想法、挑战和潜在应用，以及利用 ML 方法定制强大沉浸式体验的好处，还讨论了未来通过架构建模识别用户情感识别的潜在进展，以及如何为虚拟环境增强 ML 技术。
{"title":"Enhancing the Potential of Machine Learning for Immersive Emotion Recognition in Virtual Environment","authors":"Abinaya M, V. G","doi":"10.4108/eetsis.5036","DOIUrl":"https://doi.org/10.4108/eetsis.5036","url":null,"abstract":"Emotion recognition is an immense challenge for immersive technology. In order to detect the emotions of the user, we use machine learning methods and techniques to use the potential of the Virtual Environment and to improve the user Experience. Emotion recognition plays an important role in developing realistic and emotionally immersive experiences in augmented reality (AR) and virtual reality (VR) settings by instantly adjusting interactions, content, and visuals based on the accurate detection and interpretation of users’ emotions. Immersive systems can enhance user experience through various machine learning algorithms and methods used for emotion recognition, which are examined in this article. Upon novel idea, challenges and potential applications of incorporating emotion recognition in immersive virtual environments with Machine Learning (ML) Techniques and the benefits of tailoring powerful immersive experiences with ML methods were highlighted, and also the study discusses potential advancements in identifying the user’s emotion recognition in the future by modeling an Architecture, as well as how the ML techniques were enhanced for virtual environment is discussed.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"13 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139803587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cloud technology makes it possible for users to access information from anywhere, all the time, on any device, and that is the major cause of the many different types of assaults. In principle, multiple dangers, including data leakage, information leakage, and unauthorized information accessibility, are active in cloud environment layering. Modern technological advancements are made accessible on a daily basis through cloud technology. In the cloud, access control and encryption solutions are more complicated. Because of this greater level, security flaws in online applications and systems are more likely to occur. Somewhere at the ends of the end nodes, a malignant insider can carry out protection assaults. Nevertheless, problems with user privacy and data protection on cloud-based social networking sites continue to exist. Such problems are not known to users. On that social networking site, they post a variety of images, videos, and private information that endures even after eradication. However, some of the data that has been made public was intended to be kept private; as a result, online social information has significantly increased the risk of personally identifiable information leaking. The context of cloud technology depends on the customer capabilities such as quick storing and retrieving offered through cloud computing environments. Dependable cloud providers use a number of methodologies to deliver various digital services, creating a variety of security risks. In this paper, the study of determining intrusive cyber-attacks over the online applications using the cloud data security. Restricting access to shared resources is essential to prevent hackers from stealing vulnerabilities in cloud computing to get unauthorised access to a user's activities as well as information. Gaining access to customer information and obstructing the use of cloud computing are the primary objectives of intrusions on cloud services.
{"title":"Determining Intrusion Attacks Against Online Applications Using Cloud-Based Data Security","authors":"Rekha M, Shoba Rani P","doi":"10.4108/eetsis.5028","DOIUrl":"https://doi.org/10.4108/eetsis.5028","url":null,"abstract":"Cloud technology makes it possible for users to access information from anywhere, all the time, on any device, and that is the major cause of the many different types of assaults. In principle, multiple dangers, including data leakage, information leakage, and unauthorized information accessibility, are active in cloud environment layering. Modern technological advancements are made accessible on a daily basis through cloud technology. In the cloud, access control and encryption solutions are more complicated. Because of this greater level, security flaws in online applications and systems are more likely to occur. Somewhere at the ends of the end nodes, a malignant insider can carry out protection assaults. Nevertheless, problems with user privacy and data protection on cloud-based social networking sites continue to exist. Such problems are not known to users. On that social networking site, they post a variety of images, videos, and private information that endures even after eradication. However, some of the data that has been made public was intended to be kept private; as a result, online social information has significantly increased the risk of personally identifiable information leaking. The context of cloud technology depends on the customer capabilities such as quick storing and retrieving offered through cloud computing environments. Dependable cloud providers use a number of methodologies to deliver various digital services, creating a variety of security risks. In this paper, the study of determining intrusive cyber-attacks over the online applications using the cloud data security. Restricting access to shared resources is essential to prevent hackers from stealing vulnerabilities in cloud computing to get unauthorised access to a user's activities as well as information. Gaining access to customer information and obstructing the use of cloud computing are the primary objectives of intrusions on cloud services.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"17 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139862224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The main aim of this research work is to compress grayscale images efficiently using prediction and intensity-based image compression algorithms. Image compression is useful for removing the duplication in an image to store and transmit the data in an efficient form. This research work analyzes four new schemes for gray scale lossy image compression. Among the four schemes considered, two compressive approaches are designed for Prediction Based Image Compression (PBIC) level implementation. Third approach is designed for Intensity Based Image Compression (IBIC). Finally, the previously designed PBIC and IBIC schemes lead to an Integrated Encoder. All the considered method performances are analyzed using the performance metrics. These results are compared with JPEG 2000 which is a extensively used benchmark compression encoder. The outcome of all the proposed methods is also compared with modern encoders.
{"title":"Empirical Evaluation of Coding and Inter Pixel Redundancy in still Image Compression","authors":"A. S, K. J.","doi":"10.4108/eetsis.5029","DOIUrl":"https://doi.org/10.4108/eetsis.5029","url":null,"abstract":"The main aim of this research work is to compress grayscale images efficiently using prediction and intensity-based image compression algorithms. Image compression is useful for removing the duplication in an image to store and transmit the data in an efficient form. This research work analyzes four new schemes for gray scale lossy image compression. Among the four schemes considered, two compressive approaches are designed for Prediction Based Image Compression (PBIC) level implementation. Third approach is designed for Intensity Based Image Compression (IBIC). Finally, the previously designed PBIC and IBIC schemes lead to an Integrated Encoder. All the considered method performances are analyzed using the performance metrics. These results are compared with JPEG 2000 which is a extensively used benchmark compression encoder. The outcome of all the proposed methods is also compared with modern encoders.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"13 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139864736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cloud technology makes it possible for users to access information from anywhere, all the time, on any device, and that is the major cause of the many different types of assaults. In principle, multiple dangers, including data leakage, information leakage, and unauthorized information accessibility, are active in cloud environment layering. Modern technological advancements are made accessible on a daily basis through cloud technology. In the cloud, access control and encryption solutions are more complicated. Because of this greater level, security flaws in online applications and systems are more likely to occur. Somewhere at the ends of the end nodes, a malignant insider can carry out protection assaults. Nevertheless, problems with user privacy and data protection on cloud-based social networking sites continue to exist. Such problems are not known to users. On that social networking site, they post a variety of images, videos, and private information that endures even after eradication. However, some of the data that has been made public was intended to be kept private; as a result, online social information has significantly increased the risk of personally identifiable information leaking. The context of cloud technology depends on the customer capabilities such as quick storing and retrieving offered through cloud computing environments. Dependable cloud providers use a number of methodologies to deliver various digital services, creating a variety of security risks. In this paper, the study of determining intrusive cyber-attacks over the online applications using the cloud data security. Restricting access to shared resources is essential to prevent hackers from stealing vulnerabilities in cloud computing to get unauthorised access to a user's activities as well as information. Gaining access to customer information and obstructing the use of cloud computing are the primary objectives of intrusions on cloud services.
{"title":"Determining Intrusion Attacks Against Online Applications Using Cloud-Based Data Security","authors":"Rekha M, Shoba Rani P","doi":"10.4108/eetsis.5028","DOIUrl":"https://doi.org/10.4108/eetsis.5028","url":null,"abstract":"Cloud technology makes it possible for users to access information from anywhere, all the time, on any device, and that is the major cause of the many different types of assaults. In principle, multiple dangers, including data leakage, information leakage, and unauthorized information accessibility, are active in cloud environment layering. Modern technological advancements are made accessible on a daily basis through cloud technology. In the cloud, access control and encryption solutions are more complicated. Because of this greater level, security flaws in online applications and systems are more likely to occur. Somewhere at the ends of the end nodes, a malignant insider can carry out protection assaults. Nevertheless, problems with user privacy and data protection on cloud-based social networking sites continue to exist. Such problems are not known to users. On that social networking site, they post a variety of images, videos, and private information that endures even after eradication. However, some of the data that has been made public was intended to be kept private; as a result, online social information has significantly increased the risk of personally identifiable information leaking. The context of cloud technology depends on the customer capabilities such as quick storing and retrieving offered through cloud computing environments. Dependable cloud providers use a number of methodologies to deliver various digital services, creating a variety of security risks. In this paper, the study of determining intrusive cyber-attacks over the online applications using the cloud data security. Restricting access to shared resources is essential to prevent hackers from stealing vulnerabilities in cloud computing to get unauthorised access to a user's activities as well as information. Gaining access to customer information and obstructing the use of cloud computing are the primary objectives of intrusions on cloud services.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"50 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139802396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The main aim of this research work is to compress grayscale images efficiently using prediction and intensity-based image compression algorithms. Image compression is useful for removing the duplication in an image to store and transmit the data in an efficient form. This research work analyzes four new schemes for gray scale lossy image compression. Among the four schemes considered, two compressive approaches are designed for Prediction Based Image Compression (PBIC) level implementation. Third approach is designed for Intensity Based Image Compression (IBIC). Finally, the previously designed PBIC and IBIC schemes lead to an Integrated Encoder. All the considered method performances are analyzed using the performance metrics. These results are compared with JPEG 2000 which is a extensively used benchmark compression encoder. The outcome of all the proposed methods is also compared with modern encoders.
{"title":"Empirical Evaluation of Coding and Inter Pixel Redundancy in still Image Compression","authors":"A. S, K. J.","doi":"10.4108/eetsis.5029","DOIUrl":"https://doi.org/10.4108/eetsis.5029","url":null,"abstract":"The main aim of this research work is to compress grayscale images efficiently using prediction and intensity-based image compression algorithms. Image compression is useful for removing the duplication in an image to store and transmit the data in an efficient form. This research work analyzes four new schemes for gray scale lossy image compression. Among the four schemes considered, two compressive approaches are designed for Prediction Based Image Compression (PBIC) level implementation. Third approach is designed for Intensity Based Image Compression (IBIC). Finally, the previously designed PBIC and IBIC schemes lead to an Integrated Encoder. All the considered method performances are analyzed using the performance metrics. These results are compared with JPEG 2000 which is a extensively used benchmark compression encoder. The outcome of all the proposed methods is also compared with modern encoders.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"24 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139805071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
INTRODCTION: By analyzing the problem of self-monitoring in English online learning and constructing a strategy-integrated evaluation method, we can not only enrich the theoretical research results of self-monitoring in online learning, but also improve the independent learning ability and self-monitoring ability of students in English online learning. OBJECTIVES: To address the problem of poor optimization performance of current fusion optimization methods.METHODS:This paper proposes an online learning self-monitoring strategy fusion method based on improved nuclear reaction heuristic intelligent algorithm. First, the problems and enhancement strategies of online learning self-monitoring are analyzed; then, the online learning self-monitoring strategy fusion model is constructed by improving the nuclear reaction heuristic intelligent algorithm; finally, the proposed method is verified to be effective and feasible through the analysis of simulation experiments. RESLUTS: The results show that the fusion method of learning self-monitoring strategies on the line at the 20th iteration number starts to converge to optimization with less than 0.1s optimization time, and the error of the statistical score value before and after weight optimization is controlled within 0.05. CONCLUSION:Addressing the Optimization of Convergence of Self-Monitoring Strategies for English Online Learning.
{"title":"Improved Nuclear Reaction Heuristic Intelligence Algorithm for Online Learning in Self-Monitoring Strategy Convergence","authors":"Fengjun Liu, Yang Lu, Bin Xie, Lili Ma","doi":"10.4108/eetsis.4848","DOIUrl":"https://doi.org/10.4108/eetsis.4848","url":null,"abstract":"INTRODCTION: By analyzing the problem of self-monitoring in English online learning and constructing a strategy-integrated evaluation method, we can not only enrich the theoretical research results of self-monitoring in online learning, but also improve the independent learning ability and self-monitoring ability of students in English online learning.\u0000OBJECTIVES: To address the problem of poor optimization performance of current fusion optimization methods.METHODS:This paper proposes an online learning self-monitoring strategy fusion method based on improved nuclear reaction heuristic intelligent algorithm. First, the problems and enhancement strategies of online learning self-monitoring are analyzed; then, the online learning self-monitoring strategy fusion model is constructed by improving the nuclear reaction heuristic intelligent algorithm; finally, the proposed method is verified to be effective and feasible through the analysis of simulation experiments.\u0000RESLUTS: The results show that the fusion method of learning self-monitoring strategies on the line at the 20th iteration number starts to converge to optimization with less than 0.1s optimization time, and the error of the statistical score value before and after weight optimization is controlled within 0.05.\u0000CONCLUSION:Addressing the Optimization of Convergence of Self-Monitoring Strategies for English Online Learning.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"67 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139810324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
INTRODCTION: Immersive teaching and learning methods based on virtual reality-integrated remote platforms not only allow foreign language learners to learn in a vivid and intuitive learning environment, but also provide good conditions for multi-channel perceptual experiences of foreign language learners in terms of sight, sound and touch.OBJECTIVES: To address the problems of insufficiently systematic analysis and quantification, poor robustness and low accuracy of analysis methods in current effect analysis methods.METHODS: This paper proposes an effect analysis method of virtual reality fusion remote platform based on crawfish optimization algorithm to improve echo state network. First, the effect analysis system is constructed by analyzing the process of virtual reality fusion remote platform and extracting the effect analysis influencing elements; then, the echo state network is improved by the crayfish optimization algorithm and the effect analysis model is constructed; finally, the high accuracy of the proposed method is verified by the analysis of simulation experiments.RESLUTS: The proposed method improves the accuracy of the virtual reality fusion remote platform effect analysis model, the analysis time is 0.002s, which meets the real-time requirements, and the number of optimization convergence iterations is 16, which is better than other algorithms.CONCLUSION: The problems of insufficiently systematic analytical quantification of effect analysis methods, poor robustness of analytical methods, and low accuracy have been solved.
{"title":"A Method of Applying Virtual Reality Converged Remote Platform Based on Crawfish Optimization Algorithm to Improve ESN Network","authors":"Lili Ma, Bin Xie, Fengjun Liu, Liying Ma","doi":"10.4108/eetsis.4844","DOIUrl":"https://doi.org/10.4108/eetsis.4844","url":null,"abstract":"INTRODCTION: Immersive teaching and learning methods based on virtual reality-integrated remote platforms not only allow foreign language learners to learn in a vivid and intuitive learning environment, but also provide good conditions for multi-channel perceptual experiences of foreign language learners in terms of sight, sound and touch.OBJECTIVES: To address the problems of insufficiently systematic analysis and quantification, poor robustness and low accuracy of analysis methods in current effect analysis methods.METHODS: This paper proposes an effect analysis method of virtual reality fusion remote platform based on crawfish optimization algorithm to improve echo state network. First, the effect analysis system is constructed by analyzing the process of virtual reality fusion remote platform and extracting the effect analysis influencing elements; then, the echo state network is improved by the crayfish optimization algorithm and the effect analysis model is constructed; finally, the high accuracy of the proposed method is verified by the analysis of simulation experiments.RESLUTS: The proposed method improves the accuracy of the virtual reality fusion remote platform effect analysis model, the analysis time is 0.002s, which meets the real-time requirements, and the number of optimization convergence iterations is 16, which is better than other algorithms.CONCLUSION: The problems of insufficiently systematic analytical quantification of effect analysis methods, poor robustness of analytical methods, and low accuracy have been solved.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"136 34","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139810455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
INTRODUCTION: Accurate and objective human resources performance management evaluation methods are conducive to a comprehensive understanding of the real and objective situation of teachers, and are conducive to identifying the management, teaching and academic level of teachers, which enables teacher managers to have a clear understanding of the gaps and problems among teachers. OBJECTIVES: Aiming at the current human resources performance management evaluation method, there are evaluation indexes exist objectivity is not strong, poor precision, single method and other problems. METHODS: This research puts forward an intelligent optimisation algorithm based on the improvement of the depth of the limit of the learning machine network of human resources performance management evaluation method. (1) Through the analysis of the problems existing in the current human resources performance management, select the human resources performance management evaluation indexes, and construct the human resources performance management evaluation system; (2) Through the multi-strategy grey wolf optimization algorithm method to improve the deep learning network, and construct the evaluation model of the human resources performance management in colleges; (3) The analysis of simulation experiments verifies the high precision and real-time nature of the proposed method. RESULTS: The results show that the proposed method improves the precision of the evaluation model, improves the prediction time. CONCLUSION: This research solves the problems of low precision and non-objective system indicators of human resource performance management evaluation.
{"title":"Research on Employee Performance Management Method Based on Big Data Improvement GWO-DELM Algorithms","authors":"Zhuyu Wang, Yue Liu","doi":"10.4108/eetsis.4916","DOIUrl":"https://doi.org/10.4108/eetsis.4916","url":null,"abstract":"INTRODUCTION: Accurate and objective human resources performance management evaluation methods are conducive to a comprehensive understanding of the real and objective situation of teachers, and are conducive to identifying the management, teaching and academic level of teachers, which enables teacher managers to have a clear understanding of the gaps and problems among teachers.\u0000OBJECTIVES: Aiming at the current human resources performance management evaluation method, there are evaluation indexes exist objectivity is not strong, poor precision, single method and other problems.\u0000METHODS: This research puts forward an intelligent optimisation algorithm based on the improvement of the depth of the limit of the learning machine network of human resources performance management evaluation method. (1) Through the analysis of the problems existing in the current human resources performance management, select the human resources performance management evaluation indexes, and construct the human resources performance management evaluation system; (2) Through the multi-strategy grey wolf optimization algorithm method to improve the deep learning network, and construct the evaluation model of the human resources performance management in colleges; (3) The analysis of simulation experiments verifies the high precision and real-time nature of the proposed method.\u0000RESULTS: The results show that the proposed method improves the precision of the evaluation model, improves the prediction time.\u0000CONCLUSION: This research solves the problems of low precision and non-objective system indicators of human resource performance management evaluation.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"78 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139810874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
INTRODCTION: Listening strategy analysis and assessment not only need objective and fair sound listening strategy analysis, but also need high-precision and high real-time assessment model, and even more need in-depth analysis and feature extraction of the influencing factors of listening assessment.OBJECTIVES: To address the problems of current automatic assessment methods, such as non-specific application, poor generalization, low assessment accuracy, and poor real-time performance.METHODS: This paper proposes an automatic assessment method based on a deep confidence network based on crawfish optimization algorithm. First, the multi-dimensional listening strategy evaluation system is constructed by analyzing the listening improvement strategy; then, the depth confidence network is improved by the crayfish optimization algorithm to construct the automatic evaluation model; finally, through the analysis of simulation experiments.RESLUTS: The proposed method improves the evaluation accuracy, robustness, and real-time performance. The absolute value of the relative error of the automatic evaluation value of the proposed method is controlled in the range of 0.011, and the evaluation time is less than 0.005 s. The method is based on a deep confidence network based on the crayfish optimization algorithm.CONCLUSION: The problems of non-specific application of automated assessment methods, poor generalization, low assessment accuracy, and poor real-time performance are addressed.
{"title":"Design and Use of Deep Confidence Network Based on Crayfish Optimization Algorithm in Automatic Assessment Method of Hearing Effectiveness","authors":"Ying Cheng","doi":"10.4108/eetsis.4847","DOIUrl":"https://doi.org/10.4108/eetsis.4847","url":null,"abstract":"INTRODCTION: Listening strategy analysis and assessment not only need objective and fair sound listening strategy analysis, but also need high-precision and high real-time assessment model, and even more need in-depth analysis and feature extraction of the influencing factors of listening assessment.OBJECTIVES: To address the problems of current automatic assessment methods, such as non-specific application, poor generalization, low assessment accuracy, and poor real-time performance.METHODS: This paper proposes an automatic assessment method based on a deep confidence network based on crawfish optimization algorithm. First, the multi-dimensional listening strategy evaluation system is constructed by analyzing the listening improvement strategy; then, the depth confidence network is improved by the crayfish optimization algorithm to construct the automatic evaluation model; finally, through the analysis of simulation experiments.RESLUTS: The proposed method improves the evaluation accuracy, robustness, and real-time performance. The absolute value of the relative error of the automatic evaluation value of the proposed method is controlled in the range of 0.011, and the evaluation time is less than 0.005 s. The method is based on a deep confidence network based on the crayfish optimization algorithm.CONCLUSION: The problems of non-specific application of automated assessment methods, poor generalization, low assessment accuracy, and poor real-time performance are addressed. ","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"21 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139809078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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